Agentic AI and the race to a touchless revenue cycle

Key Take Aways

1. Agentic AI has the potential to significantly transform healthcare revenue cycle management (RCM) by enabling autonomous decision-making and end-to-end process execution.
2. Implementing agentic AI in the back end of the revenue cycle can reduce costs to collect by 30 to 60 percent, leading to substantial financial savings.
3. Automating labour-intensive tasks such as accounts receivable follow-up and denials management can increase productivity and free staff to focus on more strategic activities.
4. Starting with lower-risk, administrative back-end functions provides a safe environment for testing and refining AI deployments before tackling more complex front-end tasks.
5. Achieving a fully integrated, automated revenue cycle requires adopting a phased approach—focusing on one use case at a time and demonstrating agile value.
6. Successful deployment depends on clear success metrics and scalable solutions, with a move from pilots to enterprise-wide implementation.
7. Strategic decisions around building, buying, or partnering with technology vendors should align with organisational priorities and timelines.
8. A targeted focus on high-volume, high-error processes such as denials and underpayment management maximises initial ROI.
9. Emphasising change management and investing in workforce reskilling is essential to ensure people work effectively alongside AI.
10. Establishing AI centres of excellence supports responsible adoption, governance, and ongoing innovation.
11. Measurable operational metrics, such as denial rates and days in accounts receivable, can serve as early indicators of AI impact.
12. The ultimate goal is an interconnected network of autonomous agents across the revenue cycle, with humans in the loop to oversee and refine the process.

Key Statistics

• AI enablement could cut cost to collect by 30 to 60 percent.
• Nearly 20 percent of claims are denied on average.
• Up to 60 percent of denied claims are never appealed, resulting in revenue leakage.
• Revenue cycle costs typically account for 3 to 4 percent of a health system’s revenue, summing to over $140 billion annually.
• A health system with $6 billion in patient revenue could save between $60 million and $120 million through AI-driven efficiencies.
• Over 30 percent of providers prioritised AI and automation across the revenue cycle in 2025, compared with 4-5 use cases previously.

Key Discussion Points

1. Healthcare providers are under increasing financial pressure and are seeking operational efficiencies through innovative technology.
2. Despite long-term investments, current revenue cycle management remains fragmented and reliant on manual processes.
3. Agentic AI represents a breakthrough by enabling autonomous decision-making akin to a co-worker rather than a simple tool.
4. The back end of the revenue cycle offers a lower-risk environment for deploying agentic AI, given its rule-based and administrative nature.
5. Sequential implementation—starting with one use case—allows for manageable change and clearer demonstration of value.
6. Building trust and understanding within organisations requires early proof of concept with measurable outcomes.
7. Strategic choices around vendor partnerships, building internally, or hybrid approaches directly impact deployment speed and scale.
8. Targeting high-cost, high-error processes such as denials and cash posting maximises early benefits.
9. Workforce transformation, including establishing AI centres of excellence, ensures responsible and sustainable adoption.
10. Metrics such as denial rates, accounts receivable days, and operational efficiencies are vital for monitoring success.
11. In the longer term, interconnected autonomous agents across the revenue cycle aim to create a largely touchless, optimised system.
12. The central opportunity is transforming revenue cycle management from reactive, fragmented tasks into an efficient, adaptive, and patient-centric process.

Document Description

This article discusses how healthcare organisations can leverage agentic AI to revolutionise revenue cycle management, reducing costs, increasing efficiency, and improving patient experience. It emphasises a strategic, phased approach—starting with back-end processes—to build trust and demonstrate value, ultimately creating a fully integrated, AI-enabled revenue cycle that operates with minimal human intervention. The piece highlights practical steps for deployment, investment considerations, and the transformative potential of autonomous AI solutions in the healthcare financial landscape.